OpenRaiser/Intern-Atlas huggingface dataset:
Deep Evolution for Facial Emotion Recognition
Venue: arXiv cs.NE 2020
Year: 2020

Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack of interpretability. We propose a novel method based on evolutionary algorithms, that deals with both challenges by massively reducing the number of trainable parameters, whilst simultaneously retaining classification performance, and in some cases achieving superior performance. We are robustly able to reduce the number of parameters on average by 95% (e.g. from 2M to 100k parameters) with no loss in classification accuracy. The algorithm learns to choose small patches from the image, relative to the nose, which carry the most important information about emotion, and which coincide with typical human choices of important features. Our work implements a novel form attention and shows that evolutionary algorithms are a valuable addition to machine learning in the deep learning era, both for reducing the number of parameters for facial expression recognition and for providing interpretable features that can help reduce bias.
Temporal State Machines: Using temporal memory to stitch time-based graph computations
Venue: arXiv cs.ET 2020
Year: 2020

Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for applications ranging from dynamic programming to machine learning. However, the ad hoc mappings of algorithms into hardware result in custom architectures making them difficult to generalize. We systematize the development of race logic by associating it with the mathematical field called tropical algebra. This association between the mathematical primitives of tropical algebra and generalized race logic computations guides the design of temporally coded tropical circuits. It also serves as a framework for expressing high level timing-based algorithms. This abstraction, when combined with temporal memory, allows for the systematic generalization of race logic by making it possible to partition feed-forward computations into stages and organizing them into a state machine. We leverage analog memristor-based temporal memories to design a such a state machine that operates purely on time-coded wavefronts. We implement a version of Dijkstra's algorithm to evaluate this temporal state machine. This demonstration shows the promise of expanding the expressibility of temporal computing to enable it to deliver significant energy and throughput advantages.
Sonispace: a simulated-space interface for sound design and experimentation
Venue: arXiv cs.HC 2020
Year: 2020

The world of audio production and design has long been a difficult one to break into, requiring expertise and a working knowledge of the standard digital audio paradigms. This paper describes a novel interface that makes audio production and design more intuitive for novices, using sound-to-space relations that people have learned throughout daily life, such as the roles of barriers and distance in sound perception. The spatial interface for Sonispace allows users to quickly see the relationships between sound-emitting and sound-effecting objects, and to receive audio feedback as they make changes to the space. Algorithms were developed to resemble real-world sonic physics while being efficient enough to provide a user with immediate audio feedback. A prototype of the interface was tested by a group of participants, who confirmed that the software is accessible by novices and that the spatial interface is an engaging way of mixing audio.
Decentralized Patient Centric e-Health Record Management System using Blockchain and IPFS
Venue: arXiv cs.CR 2020
Year: 2020

Electronic Health Records(EHR) are gaining a lot of popularity all over the world. The current EHR systems however have their fair share of problems related to privacy and security. We have proposed a mechanism which provides a solution to most of these problems. Using a permissioned Ethereum blockchain allows the hospitals and patients across the world to be connected to each other. Our mechanism uses a combination of symmetric and asymmetric key cryptography to ensure the secure storage and selective access of records. It gives patients full control over their health records and also allows them to grant or revoke a hospital's access to his/her records. We have used IPFS(inter planetary file system) to store records which has the advantage of being distributed and ensures immutability of records. The proposed model also maintains the statistics of diseases without violating the privacy of any patient.
Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study
Venue: arXiv cs.CL 2020
Year: 2020

Multilingual BERT (mBERT) has shown reasonable capability for zero-shot cross-lingual transfer when fine-tuned on downstream tasks. Since mBERT is not pre-trained with explicit cross-lingual supervision, transfer performance can further be improved by aligning mBERT with cross-lingual signal. Prior work proposes several approaches to align contextualised embeddings. In this paper we analyse how different forms of cross-lingual supervision and various alignment methods influence the transfer capability of mBERT in zero-shot setting. Specifically, we compare parallel corpora vs. dictionary-based supervision and rotational vs. fine-tuning based alignment methods. We evaluate the performance of different alignment methodologies across eight languages on two tasks: Name Entity Recognition and Semantic Slot Filling. In addition, we propose a novel normalisation method which consistently improves the performance of rotation-based alignment including a notable 3% F1 improvement for distant and typologically dissimilar languages. Importantly we identify the biases of the alignment methods to the type of task and proximity to the transfer language. We also find that supervision from parallel corpus is generally superior to dictionary alignments.
Weight Prediction for Variants of Weighted Directed Networks
Venue: arXiv cs.SI 2020
Year: 2020

A weighted directed network (WDN) is a directed graph in which each edge is associated to a unique value called weight. These networks are very suitable for modeling real-world social networks in which there is an assessment of one vertex toward other vertices. One of the main problems studied in this paper is prediction of edge weights in such networks. We introduce, for the first time, a metric geometry approach to studying edge weight prediction in WDNs. We modify a usual notion of WDNs, and introduce a new type of WDNs which we coin the term \textit{almost-weighted directed networks} (AWDNs). AWDNs can capture the weight information of a network from a given training set. We then construct a class of metrics (or distances) for AWDNs which equips such networks with a metric space structure. Using the metric geometry structure of AWDNs, we propose modified $k$ nearest neighbors (kNN) methods and modified support-vector machine (SVM) methods which will then be used to predict edge weights in AWDNs. In many real-world datasets, in addition to edge weights, one can also associate weights to vertices which capture information of vertices; association of weights to vertices especially plays an important role in graph embedding problems. Adopting a similar approach, we introduce two new types of directed networks in which weights are associated to either a subset of origin vertices or a subset of terminal vertices . We, for the first time, construct novel classes of metrics on such networks, and based on these new metrics propose modified $k$NN and SVM methods for predicting weights of origins and terminals in these networks. We provide experimental results on several real-world datasets, using our geometric methodologies.
The General Law Principles for Protection the Personal Data and their Importance
Venue: arXiv cs.CR 2020
Year: 2020

Rapid technological change and globalization have created new challenges when it comes to the protection and processing of personal data. In 2018, Brazil presented a new law that has the proposal to inform how personal data should be collected and treated, to guarantee the security and integrity of the data holder. The purpose of this paper is to emphasize the principles of the General Law on Personal Data Protection, informing real cases of leakage of personal data and thus obtaining an understanding of the importance of gains that meet the interests of Internet users on the subject and its benefits to the entire Brazilian society.
Attention-Driven Body Pose Encoding for Human Activity Recognition
Venue: arXiv cs.CV 2020
Year: 2020

This article proposes a novel attention-based body pose encoding for human activity recognition that presents a enriched representation of body-pose that is learned. The enriched data complements the 3D body joint position data and improves model performance. In this paper, we propose a novel approach that learns enhanced feature representations from a given sequence of 3D body joints. To achieve this encoding, the approach exploits 1) a spatial stream which encodes the spatial relationship between various body joints at each time point to learn spatial structure involving the spatial distribution of different body joints 2) a temporal stream that learns the temporal variation of individual body joints over the entire sequence duration to present a temporally enhanced representation. Afterwards, these two pose streams are fused with a multi-head attention mechanism. % adapted from neural machine translation. We also capture the contextual information from the RGB video stream using a Inception-ResNet-V2 model combined with a multi-head attention and a bidirectional Long Short-Term Memory (LSTM) network. %Moreover, we whose performance is enhanced through the multi-head attention mechanism. Finally, the RGB video stream is combined with the fused body pose stream to give a novel end-to-end deep model for effective human activity recognition.
Ethically Collecting Multi-Modal Spontaneous Conversations with People that have Cognitive Impairments
Venue: arXiv cs.CL 2020
Year: 2020

In order to make spoken dialogue systems (such as Amazon Alexa or Google Assistant) more accessible and naturally interactive for people with cognitive impairments, appropriate data must be obtainable. Recordings of multi-modal spontaneous conversations with vulnerable user groups are scarce however and this valuable data is challenging to collect. Researchers that call for this data are commonly inexperienced in ethical and legal issues around working with vulnerable participants. Additionally, standard recording equipment is insecure and should not be used to capture sensitive data. We spent a year consulting experts on how to ethically capture and share recordings of multi-modal spontaneous conversations with vulnerable user groups. In this paper we provide guidance, collated from these experts, on how to ethically collect such data and we present a new system - "CUSCO" - to capture, transport and exchange sensitive data securely. This framework is intended to be easily followed and implemented to encourage further publications of similar corpora. Using this guide and secure recording system, researchers can review and refine their ethical measures.
Rethinking Evaluation Methodology for Audio-to-Score Alignment
Venue: arXiv cs.SD 2020
Year: 2020

This paper offers a precise, formal definition of an audio-to-score alignment. While the concept of an alignment is intuitively grasped, this precision affords us new insight into the evaluation of audio-to-score alignment algorithms. Motivated by these insights, we introduce new evaluation metrics for audio-to-score alignment. Using an alignment evaluation dataset derived from pairs of KernScores and MAESTRO performances, we study the behavior of our new metrics and the standard metrics on several classical alignment algorithms.
A General Framework for Charger Scheduling Optimization Problems
Venue: arXiv cs.NI 2020
Year: 2020

This paper presents a general framework to tackle a diverse range of NP-hard charger scheduling problems, optimizing the trajectory of mobile chargers to prolong the life of Wireless Rechargeable Sensor Network (WRSN), a system consisting of sensors with rechargeable batteries and mobile chargers. Existing solutions to charger scheduling problems require problem-specific design and a trade-off between the solution quality and computing time. Instead, we observe that instances of the same type of charger scheduling problem are solved repeatedly with similar combinatorial structure but different data. We consider searching an optimal charger scheduling as a trial and error process, and the objective function of a charging optimization problem as reward, a scalar feedback signal for each search. We propose a deep reinforcement learning-based charger scheduling optimization framework. The biggest advantage of the framework is that a diverse range of domain-specific charger scheduling strategy can be learned automatically from previous experiences. A framework also simplifies the complexity of algorithm design for individual charger scheduling optimization problem. We pick three representative charger scheduling optimization problems, design algorithms based on the proposed deep reinforcement learning framework, implement them, and compare them with existing ones. Extensive simulation results show that our algorithms based on the proposed framework outperform all existing ones.
On the Succinctness of Alternating Parity Good-for-Games Automata
Venue: arXiv cs.FL 2020
Year: 2020

We study alternating parity good-for-games (GFG) automata, i.e., alternating parity automata where both conjunctive and disjunctive choices can be resolved in an online manner, without knowledge of the suffix of the input word still to be read. We show that they can be exponentially more succinct than both their nondeterministic and universal counterparts. Furthermore, we present a single exponential determinisation procedure and an Exptime upper bound to the problem of recognising whether an alternating automaton is GFG. We also study the complexity of deciding "half-GFGness", a property specific to alternating automata that only requires nondeterministic choices to be resolved in an online manner. We show that this problem is PSpace-hard already for alternating automata on finite words.
LEBANONUPRISING: a thorough study of Lebanese tweets
Venue: arXiv cs.CL 2020
Year: 2020

Recent studies showed a huge interest in social networks sentiment analysis. Twitter, which is a microblogging service, can be a great source of information on how the users feel about a certain topic, or what their opinion is regarding a social, economic and even political matter. On October 17, Lebanon witnessed the start of a revolution; the LebanonUprising hashtag became viral on Twitter. A dataset consisting of a 100,0000 tweets was collected between 18 and 21 October. In this paper, we conducted a sentiment analysis study for the tweets in spoken Lebanese Arabic related to the LebanonUprising hashtag using different machine learning algorithms. The dataset was manually annotated to measure the precision and recall metrics and to compare between the different algorithms. Furthermore, the work completed in this paper provides two more contributions. The first is related to building a Lebanese to Modern Standard Arabic mapping dictionary that was used for the preprocessing of the tweets and the second is an attempt to move from sentiment analysis to emotion detection using emojis, and the two emotions we tried to predict were the "sarcastic" and "funny" emotions. We built a training set from the tweets collected in October 2019 and then we used this set to predict sentiments and emotions of the tweets we collected between May and August 2020. The analysis we conducted shows the variation in sentiments, emotions and users between the two datasets. The results we obtained seem satisfactory especially considering that there was no previous or similar work done involving Lebanese Arabic tweets, to our knowledge.
Asynchronous Liquids: Regional Time Stepping for Faster SPH and PCISPH
Venue: arXiv cs.GR 2020
Year: 2020

This paper presents novel and efficient strategies to spatially adapt the amount of computational effort applied based on the local dynamics of a free surface flow, for both classic weakly compressible SPH (WCSPH) and predictive-corrective incompressible SPH (PCISPH). Using a convenient and readily parallelizable block-based approach, different regions of the fluid are assigned differing time steps and solved at different rates to minimize computational cost. Our approach for WCSPH scheme extends an asynchronous SPH technique from compressible flow of astrophysical phenomena to the incompressible free surface setting, and further accelerates it by entirely decoupling the time steps of widely spaced particles. Similarly, our approach to PCISPH adjusts the the number of iterations of density correction applied to different regions, and asynchronously updates the neighborhood regions used to perform these corrections; this sharply reduces the computational cost of slowly deforming regions while preserving the standard density invariant. We demonstrate our approaches on a number of highly dynamic scenarios, demonstrating that they can typically double the speed of a simulation compared to standard methods while achieving visually consistent results.
What We See and What We Get from Visualization: Eye Tracking Beyond Gaze Distributions and Scanpaths
Venue: arXiv cs.HC 2020
Year: 2020

Technical progress in hardware and software enables us to record gaze data in everyday situations and over long time spans. Among a multitude of research opportunities, this technology enables visualization researchers to catch a glimpse behind performance measures and into the perceptual and cognitive processes of people using visualization techniques. The majority of eye tracking studies performed for visualization research is limited to the analysis of gaze distributions and aggregated statistics, thus only covering a small portion of insights that can be derived from gaze data. We argue that incorporating theories and methodology from psychology and cognitive science will benefit the design and evaluation of eye tracking experiments for visualization. This position paper outlines our experiences with eye tracking in visualization and states the benefits that an interdisciplinary research field on visualization psychology might bring for better understanding how people interpret visualizations.
Uncertainty Estimation For Community Standards Violation In Online Social Networks
Venue: arXiv cs.AI 2020
Year: 2020

Online Social Networks (OSNs) provide a platform for users to share their thoughts and opinions with their community of friends or to the general public. In order to keep the platform safe for all users, as well as to keep it compliant with local laws, OSNs typically create a set of community standards organized into policy groups, and use Machine Learning (ML) models to identify and remove content that violates any of the policies. However, out of the billions of content that is uploaded on a daily basis only a small fraction is so unambiguously violating that it can be removed by the automated models. Prevalence estimation is the task of estimating the fraction of violating content in the residual items by sending a small sample of these items to human labelers to get ground truth labels. This task is exceedingly hard because even though we can easily get the ML scores or features for all of the billions of items we can only get ground truth labels on a few thousands of these items due to practical considerations. Indeed the prevalence can be so low that even after a judicious choice of items to be labeled there can be many days in which not even a single item is labeled violating. A pragmatic choice for such low prevalence, $10^{-4}$ to $10^{-5}$, regimes is to report the upper bound, or $97.5\%$ confidence interval, prevalence (UBP) that takes the uncertainties of the sampling and labeling processes into account and gives a smoothed estimate. In this work we present two novel techniques Bucketed-Beta-Binomial and a Bucketed-Gaussian Process for this UBP task and demonstrate on real and simulated data that it has much better coverage than the commonly used bootstrapping technique.
A robustness measure for singular point and index estimation in discretized orientation and vector fields
Venue: arXiv cs.CV 2020
Year: 2020

The identification of singular points or topological defects in discretized vector fields occurs in diverse areas ranging from the polarization of the cosmic microwave background to liquid crystals to fingerprint recognition and bio-medical imaging. Due to their discrete nature, defects and their topological charge cannot depend continuously on each single vector, but they discontinuously change as soon as a vector changes by more than a threshold. Considering this threshold of admissible change at the level of vectors, we develop a robustness measure for discrete defect estimators. Here, we compare different template paths for defect estimation in discretized vector or orientation fields. Sampling prototypical vector field patterns around defects shows that the robustness increases with the length of template path, but less so in the presence of noise on the vectors. We therefore find an optimal trade-off between resolution and robustness against noise for relatively small templates, except for the "single pixel" defect analysis, which cannot exclude zero robustness. The presented robustness measure paves the way for uncertainty quantification of defects in discretized vector fields.
Meta Partial Benders Decomposition for the Logistics Service Network Design Problem
Venue: arXiv cs.RO 2020
Year: 2020

Supply chain transportation operations often account for a large proportion of product total cost to market. Such operations can be optimized by solving the Logistics Service Network Design Problem (LSNDP), wherein a logistics service provider seeks to cost-effectively source and fulfill customer demands of products within a multi-echelon distribution network. However, many industrial settings yield instances of the LSNDP that are too large to be solved in reasonable run-times by off-the-shelf optimization solvers. We introduce an exact Benders decomposition algorithm based on partial decompositions that strengthen the master problem with information derived from aggregating subproblem data. More specifically, the proposed Meta Partial Benders Decomposition intelligently switches from one master problem to another by changing both the amount of subproblem information to include in the master as well as how it is aggregated. Through an extensive computational study, we show that the approach outperforms existing benchmark methods and we demonstrate the benefits of dynamically refining the master problem in the course of a partial Benders decomposition-based scheme.
Adversarial Semi-Supervised Multi-Domain Tracking
Venue: arXiv cs.CV 2020
Year: 2020

Neural networks for multi-domain learning empowers an effective combination of information from different domains by sharing and co-learning the parameters. In visual tracking, the emerging features in shared layers of a multi-domain tracker, trained on various sequences, are crucial for tracking in unseen videos. Yet, in a fully shared architecture, some of the emerging features are useful only in a specific domain, reducing the generalization of the learned feature representation. We propose a semi-supervised learning scheme to separate domain-invariant and domain-specific features using adversarial learning, to encourage mutual exclusion between them, and to leverage self-supervised learning for enhancing the shared features using the unlabeled reservoir. By employing these features and training dedicated layers for each sequence, we build a tracker that performs exceptionally on different types of videos.